Foreign Direct Investment and Financial Development in Guinea: Causality Analysis

This essay provides a better comprehension of the other disregarded impacts of FDI by examining first, the causality direction then the long- and short-term interaction among inward FDI and financial development in Guinea using 1990-2017 data set. The empirical assertions are grounded on the Granger causality wald test, Bounds test for co-integration, Error correction model (ECM) and the Auto regressive distributed lag (ARDL) framework. FDI per GDP net inflows and Credit to private sector are respectively adopted as FDI measure and financial advancement indicator. The following outcomes are established: first, FDI in the long term negatively influence financial advancement in Guinea at 5% magnitude. This inference indicates that 1 percent surge in FDI per GDP induces 0.389 decrease in credit to private sector. Second, FDI per GDP [L1] negatively and significantly interact with financial advancement in the short term. Suggesting that 1 percent increase in FDI in the short term engenders 0.215 decrease in credit to private sector. Third, the causality direction remains unidirectional irrespective to the number of lags. Finally, the long- and short-term coefficients tell us the same story regardless of the time effects. Overall, contrary to the common perceptions, we found strong evidence that foreign investment does not enhance financial development in Guinea. In terms of practical implications, it seems ineffective to use FDI as financial advancement instrument within the Guinean context.

COVID-19's Impact on China's Strategic Emerging Industries: An Observation of Policy Difficulties

The study investigates the influence of the COVID-19 on the rate of R&D investment and foreign exchange development of China's most important emerging industry firms. From 2010 to 2020, data were collected from 26 locations across China, focusing on seven different types of critical creating companies. To analyze the data, we have applied Fourier Increased Unit Root Test, Granger causality assessments test, Pattern Assessment test, Poisson pseudo most excellent probability (PPML) approach, Wald test, and Regression analysis test. The results of the tests reveal a clear underlying association among COVID-19 relates Chinese exports and imports. COVID-19's instant effects on imports and exports lack working capital have been calculated, but the short-term, medium-to-long-term products are composite and unidentified. The article result main results are following: (i) The COVID-19 impacts the R&D investment is main industries like as high-end equipment industry, new materials industry, and new-era data innovation. (ii) The COVID-19 highly affects the imports and exports development network of Chinese strategic emerging industries which emphasizes cross-industry grouping features. The study provides the guidance to the future researchers to focus on COVID-19 affects on the strategic emerging industries of developed and underdeveloped countries to determine of foreign direct investment inflow and unemployment growth rates.JEL: G20, O10, O40

Panel Regression Models for Paddy (Oryza sativa) Crop Production

The present investigation was carried out to study area production trends of Paddy crop grown in different districts of Tamil Nadu state, India during the period 1998-99 to 2010- 2020 based on Panel Regression Model. The statistically most suited Panel Regression model was selected based on Hausman and Wald test. The study variables namely the area under the Paddy crop (AREA) and the production (PRODN) of Paddy crop were found to be stationary at level. Analysis of variance test indicated that district to district crop productions were highly significant. Highest area under the crops and productions were registered in Tiruvarur, Thanjavur etc., Very lowest were registered in Coimbatore and Nilgiris districts. The fixed effect model was found to be suitable to study the trend and this model explains the 87% of variations in Paddy crop production.

Apakah Empati Mempengaruhi Kepatuhan Pajak?

Research on the impact of empathy as a non-economic factor on taxpayer compliance has not been studied in Indonesia. Most research still focuses on economic factors that affect tax compliance. To find out whether empathy affects tax compliance, this study uses binary logistic regression analysis because the dependent variable is nominal and categorical. The significance of the regression equation parameters was tested by the Ratio Likelihood Test and the Wald Test. Then, the goodness of fit model was tested using the Hosmer-Lemeshow Test. The likelihood ratio test shows that there is at least one coefficient from the regression model that has an effect on the 5% significance level. Based on the Wald test, there is no independent variable that has a significant effect on tax compliance with a 5% significance level. The Hosmer-Lemeshow test states that the binary logistic regression model in this study is feasible to use. Binary logistic regression analysis shows that only one predictor has a significant effect on the tax compliance variable, namely empathic concern items. The odds ratio value of 1.054 on the empathic concern items variable shows that taxpayers who have empathy will be 1.054 times more tax compliant than respondents who do not have empathy. In other words, respondents who have empathy will tend to be more obedient in paying taxes than those who do not have this trait. Therefore, the government can formulate regulations that support the emergence of empathic concern items in the community so that tax compliance increases.

Species-Specific Responses of Bird Song Output in the Presence of Drones

Drones are now widely used to study wildlife, but their application in the study of bioacoustics is limited. Drones can be used to collect data on bird vocalizations, but an ongoing concern is that noise from drones could change bird vocalization behavior. To test for behavioral impact, we conducted an experiment using 30 sound localization arrays to track the song output of 7 songbird species before, during, and after a 3 min flight of a small quadcopter drone hovering 48 m above ground level. We analyzed 8303 song bouts, of which 2285, from 184 individual birds were within 50 m of the array centers. We used linear mixed effect models to assess whether patterns in bird song output could be attributed to the drone’s presence. We found no evidence of any effect of the drone on five species: American Robin Turdus migratorius, Common Yellowthroat Geothlypis trichas, Field Sparrow Spizella pusilla, Song Sparrow Melospiza melodia, and Indigo Bunting Passerina cyanea. However, we found a substantial decrease in Yellow Warbler Setophaga petechia song detections during the 3 min drone hover; there was an 81% drop in detections in the third minute (Wald test, p < 0.001) compared with before the drone’s introduction. By contrast, the number of singing Northern Cardinal Cardinalis cardinalis increased when the drone was overhead and remained almost five-fold higher for 4 min after the drone departed (p < 0.001). Further, we found an increase in cardinal contact/alarm calls when the drone was overhead, with the elevated calling rate lasting for 2 min after the drone departed (p < 0.001). Our study suggests that the responses of songbirds to drones may be species-specific, an important consideration when proposing the use of drones in avian studies. We note that recent advances in drone technology have resulted in much quieter drones, which makes us hopeful that the impact that we detected could be greatly reduced.

Integrating informative hypotheses into the EffectLiteR framework

Using the EffectLiteR framework, researchers can test classical null hypotheses about effects of interest via Wald and F-tests, while taking into account the stochastic nature of group sizes. This paper aims at extending EffectLiteR to test informative hypotheses, assuming for example that the average effect of a new treatment is greater than the average effect of an old treatment, which in turn is greater than zero. We present a simulated data example to show two methodological novelties. First, we illustrate how to use the Fbar- and generalized linear Wald test to assess informative hypotheses. While the classical test did not reach significance, the informative test correctly rejected the null hypothesis, indicating the need to take into account the order of the treatment groups. Second, we demonstrate how to account for stochastic group sizes in informative hypotheses using the generalized non-linear Wald statistic. The paper concludes with a short data example.

Impact of Agriculture and Energy on CO2 Emissions in Zambia

The world has experienced increased impacts of anthropogenic global warming due to increased emissions of greenhouse gases (GHGs), which include carbon dioxide (CO2). Anthropogenic activities that contribute to CO2 emissions include deforestation, usage of fertilizers, and activities related to mining and energy production. The main objective of this paper was to assess the impacts of agriculture and energy production on CO2 emissions in Zambia. This research used econometric analysis, specifically the Autoregressive-Distributed Lag (ARDL) Bounds Test, to analyze the relationship between CO2 emissions and GDP, electricity consumption, agricultural production, and industry value added. The results showed the presence of cointegration, where the variables of CO2 emissions, GDP, electricity, and agriculture converge to a long-run equilibrium at the rate of 74%. Further, there was a short-run causality towards CO2 emissions running from agriculture and the consumption of energy as indicated by the Wald test. This is the first study of its kind that empirically shows the impact of agricultural activities and energy consumption on the Zambian environment through their contribution to CO2 emissions at a macro (country) level. This paper also presents recommendations that are pertinent to mitigate these effects. To deescalate environmental degradation, we propose increasing the number of access points for multiple renewable energy sources across the country; discouraging deforestation, the usage of conventional fertilizers, and the burning of vegetation for fertilizers; encouraging afforestation and reforestation, in addition to providing subsidies, training, and financial support to farmers and entrepreneurs who decide to use environmentally friendly agricultural methods and renewable energy. This research highlights the serious impacts of anthropogenic activities on CO2 emissions. The study was intended to assist Zambian policymakers in formulating and implementing environmentally friendly policy measures or systems that will contribute towards environmental protection commitments and sustainable economic development.

Impact of Economic Structure on the Environmental Kuznets Curve (EKC) hypothesis in India

This study aims to evaluate the impact of economic structure on the Environmental Kuznets Curve (EKC) in India. The present study deviates from the bulk of study in the literature with the incorporation of both aggregated and disaggregated measures of economic development on the environmental degradation function. For the empirical analysis, the study employed the Auto-Regressive Distributed Lag (ARDL) bounds testing approach of cointegration to analyse the long-run and short-run relationship during 1971–2014. Further, the direction of the causality is investigated through the Wald test approach. The results revealed that the conventional EKC hypothesis does not hold in India in both aggregated and disaggregated models since economic growth and its component have a U-shaped impact on the environmental quality in India. However, the effect of population on environmental quality is positive but not significant in the aggregated model. Whereas, in the disaggregated model, it is significantly affecting environmental quality. Hence, it is possible to infer that the population of the country increases, the demand for energy consumption increase tremendously, particularly consumption of fossil fuel like coal, oil, and natural gas, and is also evident from the energy structure coefficient from both models. This increase is due to the scarcity of renewable energy for meeting the needs of people. On the contrary, urbanization reduces environmental degradation, which may be due to improved living conditions in terms of efficient infrastructure and energy efficiency in the urban area leading to a negative relation between urbanization and environmental degradation.

Factors Associated With Engaging in Regular Physical Activity Among Women Family Caregivers

Regular physical activity is important for promoting the health of family caregivers. In this study, we used data from the 2015 and 2017 Behavioral Risk Factor Surveillance System Questionnaire-Caregiver module to examine factors associated with meeting physical activity guidelines among women caregivers. Meeting physical activity guidelines was defined as participating in 150 minutes (or vigorous equivalent minutes) of physical activity weekly. We used survey-weighted multivariate regression analyses to examine relationships between sociodemographic, caregiving, and health characteristics and meeting physical activity guidelines. All variables were entered into the model simultaneously. The Wald test was used to test the significance of interactions between race and ethnicity and other covariates. 50.7% of 10,542 women caregivers met physical activity guidelines. The amount of time spent caregiving each week was not associated with the odds of meeting guidelines. Caregivers in the paid workforce had decreased odds (OR=0.73, 95%CI [0.62-0.87]) of meeting guidelines. Compared to women caregiving for &lt;6months, women caregiving for 6 months to 2 years had increased odds of meeting guidelines (OR =1.33, 95%CI [1.08-1.64]). Increasing education was associated with an increased odds of meeting guidelines, but being college educated had a more positive effect for Hispanic than white caregivers (pinteraction=0.03). Having children did not affect the odds of meeting guidelines for white caregivers, but for black caregivers having two or more children decreased the odds (pinteraction=0.03). Understanding how sociodemographic, caregiving, and health characteristics impact engagement in regular physical activity is critical to designing effective interventions and ultimately improving the health of caregivers.

Cyprus Economy in the Light of Debt Problem

The model created by using the independent variables of total income, total capital, total savings, government expenditures, and employment, which I think has a significant impact on the growth of the Cyprus economy, has been examined in the light of the debt problem. Annual time-series data from 1995Q to 2017Q were obtained from the Cyprus State Planning Office in Cyprus. Unrestricted VAR (Vector Autoregression) model was used to test the causal relationship of the variables considered. Empirical findings revealed that some variables such as Wald test results for 78 lags, respectively, affect the GDP growth rate together. In particular, it was observed that there are bidirectional influences between employment, government expenditures, total capital, and savings which are not estimated in former studies. In addition, total income and total savings coefficients have a unidirectional influence on employment. It has been observed that the expenditure and savings coefficients also affect the total income.